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The prevalence and impact of university affiliation discrepancies between four bibliographic databases—Scopus, Web of Science, Dimensions, and Microsoft Academic
Research managers benchmarking universities against international peers face the problem of
affiliation disambiguation. Different databases have taken separate approaches to this problem
and discrepancies exist between them. Bibliometric data sources typically conduct a
disambiguation process that unifies variant institutional names and those of its subunits so that
researchers can then search all records from that institution using a single unified name. This
study examined affiliation discrepancies between Scopus, Web of Science, Dimensions, and
Microsoft Academic for 18 Arab universities over a five-year period. We confirmed that digital
object identifiers (DOIs) are suitable for extracting comparable scholarly material across
databases and quantified the affiliation discrepancies between them. A substantial share of
records assigned to the selected universities in any one database were not assigned to the same
Show moreResearch managers benchmarking universities against international peers face the problem of
affiliation disambiguation. Different databases have taken separate approaches to this problem
and discrepancies exist between them. Bibliometric data sources typically conduct a
disambiguation process that unifies variant institutional names and those of its subunits so that
researchers can then search all records from that institution using a single unified name. This
study examined affiliation discrepancies between Scopus, Web of Science, Dimensions, and
Microsoft Academic for 18 Arab universities over a five-year period. We confirmed that digital
object identifiers (DOIs) are suitable for extracting comparable scholarly material across
databases and quantified the affiliation discrepancies between them. A substantial share of
records assigned to the selected universities in any one database were not assigned to the same
university in another. The share of discrepancy was higher in the larger databases (Dimensions
and Microsoft Academic). The smaller, more selective databases (Scopus and especially Web
of Science) tended to agree to a greater degree with affiliations in the other databases. Manual
examination of affiliation discrepancies showed that they were caused by a mixture of missing
affiliations, unification differences, and assignation of records to the wrong institution.
Show less- All authors
- Purnell, P.J.
- Date
- 2022-04-12
- Journal
- Quantitative Science Studies
- Volume
- 3
- Issue
- 1
- Pages
- 99 - 121